A deterministic annealing neural network for convex programming
Neural Networks
Generalized convexity of functions and generalized monotonicity of set-valued maps
Journal of Optimization Theory and Applications
Differential Inclusions: Set-Valued Maps and Viability Theory
Differential Inclusions: Set-Valued Maps and Viability Theory
Robustness of convergence in finite time for linear programming neural networks: Research Articles
International Journal of Circuit Theory and Applications
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Primal and dual neural networks for shortest-path routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
Primal and dual assignment networks
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
Generalized recurrent neural network for ε-insensitive support vector regression
Mathematics and Computers in Simulation
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In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed.